A Robust and Accurate End-to-End Template Matching Method Based on the Siamese Network

Abstract

Template matching is an important and challenging task in remote sensing and computer vision. Existing template matching methods often fail in the presence of complex nonrigid deformation, occlusion, and background clutter. In this letter, inspired by Siamese trackers, we propose an end-to-end template matching method that is based on the Siamese network. Different from the traditional template matching methods, our method treats the template matching task as a classification-regression task. It is more robust to background clutter, occlusion, and nonrigid deformation. Moreover, we introduce a channel-attention mechanism in the cross correlation operation and replace the commonly used intersection-over-union (IoU) with distance-IoU (DIoU) to build a new regression loss, which further improves the performance of our method. Extensive experiments on the commonly used public benchmark demonstrate that our method achieves the state-of-the-art performance.